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Proceedings Paper

Statistical approach utilizing neural networks for CD error prediction
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Paper Abstract

We studied a three-layer backpropagation neural network to describe nonlinear relationships between inputs (error sources/control knobs) and output CDs. The application of the neural network to modeling of optical proximity effect for a 65nm node CMOS gate layer was investigated. The prediction accuracy of the neural network was improved with the increase in the training data size, becoming higher than that of a conventional lithography simulation with a lumped parameter model. The result suggests that neural networks trained with a sufficient amount of data can provide the same or higher accuracy for the CD error prediction than physical model-based approaches. The use of information of aerial images as input parameters improved the accuracy. Also, pattern density effects, which are difficult to treat by a conventional lithography simulation, could be successfully reflected in the CD error prediction. Using lot data over a period of time, we trained a neural network in which the exposure parameters and lot mean CDs were inputs and outputs, respectively. From the network, lot mean CDs for successive periods were able to be predicted. From these results, we conclude that the application of neural networks for CD control in advanced lithography is worth developing.

Paper Details

Date Published: 5 April 2007
PDF: 8 pages
Proc. SPIE 6518, Metrology, Inspection, and Process Control for Microlithography XXI, 651812 (5 April 2007); doi: 10.1117/12.712115
Show Author Affiliations
Masafumi Asano, Toshiba Corp. (Japan)
Masaki Satake, Toshiba Corp. (Japan)
Satoshi Tanaka, Toshiba Corp. (Japan)
Shoji Mimotogi, Toshiba Corp. (Japan)


Published in SPIE Proceedings Vol. 6518:
Metrology, Inspection, and Process Control for Microlithography XXI
Chas N. Archie, Editor(s)

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